Mean absolute error (MAE) is the main metric we use for evaluating the performance of the models. MAE is defined as the mean of the absolute difference between the predicted time of an event and the observed time for that event for uncensored data samples. We consider the expected value of a predicted PDF as the predicted event time. The other metric used in this paper is the time-dependent CI score [1]} defined as follows:
\(\mathbb {CI}(t)=P(\hat{F}(t|x_i) > \hat{F}(t|x_j)| \delta _i =1, T_i<T_j, T_i\le t),\) 
